[Tutorialsplanet.NET] Udemy - PyTorch Deep Learning and Artificial Intelligence

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[Tutorialsplanet.NET] Udemy - PyTorch Deep Learning and Artificial Intelligence

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种子哈希：5553f1e5099ac69838af28dc223a4afb557cb477
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收录时间：2021-05-30
最近下载：2024-08-16

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文件列表

  • 18. Setting up your Environment (FAQ by Student Request)/2. Windows-Focused Environment Setup 2018.mp4 189.5 MB
  • 18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 175.5 MB
  • 18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 158.0 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 119.8 MB
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 113.5 MB
  • 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 111.5 MB
  • 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 110.0 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 97.1 MB
  • 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 96.6 MB
  • 2. Google Colab/2. Uploading your own data to Google Colab.mp4 94.9 MB
  • 5. Convolutional Neural Networks/5. CNN Architecture.mp4 93.9 MB
  • 4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 93.6 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 90.9 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 85.1 MB
  • 4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 84.1 MB
  • 1. Introduction/2. Overview and Outline.mp4 83.5 MB
  • 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 83.5 MB
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 83.5 MB
  • 3. Machine Learning and Neurons/6. Moore's Law Notebook.mp4 82.7 MB
  • 3. Machine Learning and Neurons/10. Classification Notebook.mp4 82.1 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).mp4 81.6 MB
  • 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 81.6 MB
  • 5. Convolutional Neural Networks/13. Improving CIFAR-10 Results.mp4 81.2 MB
  • 8. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).mp4 80.6 MB
  • 5. Convolutional Neural Networks/6. CNN Code Preparation (part 1).mp4 80.5 MB
  • 5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 80.1 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 79.8 MB
  • 4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 79.1 MB
  • 5. Convolutional Neural Networks/9. CNN for Fashion MNIST.mp4 78.1 MB
  • 3. Machine Learning and Neurons/2. Regression Basics.mp4 76.6 MB
  • 3. Machine Learning and Neurons/4. Regression Notebook.mp4 75.4 MB
  • 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code Yourself (part 1).mp4 75.4 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 75.3 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).mp4 74.5 MB
  • 3. Machine Learning and Neurons/1. What is Machine Learning.mp4 74.0 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 73.4 MB
  • 8. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).mp4 73.0 MB
  • 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 72.9 MB
  • 3. Machine Learning and Neurons/8. Linear Classification Basics.mp4 70.5 MB
  • 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 70.0 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 69.6 MB
  • 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 69.3 MB
  • 7. Natural Language Processing (NLP)/6. Text Classification with LSTMs.mp4 68.2 MB
  • 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 67.9 MB
  • 10. GANs (Generative Adversarial Networks)/3. GAN Code.mp4 64.4 MB
  • 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 63.4 MB
  • 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 63.2 MB
  • 7. Natural Language Processing (NLP)/1. Embeddings.mp4 62.9 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 61.4 MB
  • 7. Natural Language Processing (NLP)/7. CNNs for Text.mp4 61.4 MB
  • 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 61.0 MB
  • 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 59.8 MB
  • 5. Convolutional Neural Networks/10. CNN for CIFAR-10.mp4 59.5 MB
  • 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 59.2 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 59.1 MB
  • 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 59.1 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 58.4 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 58.0 MB
  • 16. In-Depth Gradient Descent/5. Adam (pt 1).mp4 57.9 MB
  • 16. In-Depth Gradient Descent/6. Adam (pt 2).mp4 55.3 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 55.2 MB
  • 7. Natural Language Processing (NLP)/3. Text Preprocessing (pt 1).mp4 54.8 MB
  • 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 54.8 MB
  • 3. Machine Learning and Neurons/16. Train Sets vs. Validation Sets vs. Test Sets.mp4 54.7 MB
  • 14. VIP Facial Recognition/9. Accuracy and imbalanced classes.mp4 53.6 MB
  • 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 53.0 MB
  • 14. VIP Facial Recognition/2. Siamese Networks.mp4 53.0 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 52.8 MB
  • 3. Machine Learning and Neurons/14. How does a model learn.mp4 52.5 MB
  • 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 2).mp4 51.5 MB
  • 7. Natural Language Processing (NLP)/9. VIP Making Predictions with a Trained NLP Model.mp4 51.2 MB
  • 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 51.1 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 50.9 MB
  • 7. Natural Language Processing (NLP)/5. Text Preprocessing (pt 3).mp4 50.1 MB
  • 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 50.0 MB
  • 4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 49.4 MB
  • 3. Machine Learning and Neurons/3. Regression Code Preparation.mp4 47.7 MB
  • 3. Machine Learning and Neurons/13. A Short Neuroscience Primer.mp4 46.8 MB
  • 5. Convolutional Neural Networks/11. Data Augmentation.mp4 46.7 MB
  • 7. Natural Language Processing (NLP)/4. Text Preprocessing (pt 2).mp4 46.6 MB
  • 2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 46.5 MB
  • 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 46.3 MB
  • 13. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.mp4 45.7 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).mp4 45.3 MB
  • 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 45.0 MB
  • 13. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.mp4 44.8 MB
  • 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 43.5 MB
  • 9. Transfer Learning for Computer Vision/3. Large Datasets.mp4 43.3 MB
  • 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 42.6 MB
  • 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 42.2 MB
  • 8. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.mp4 42.1 MB
  • 7. Natural Language Processing (NLP)/8. Text Classification with CNNs.mp4 41.2 MB
  • 21. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.6 MB
  • 5. Convolutional Neural Networks/7. CNN Code Preparation (part 2).mp4 38.5 MB
  • 1. Introduction/1. Welcome.mp4 37.5 MB
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 37.0 MB
  • 14. VIP Facial Recognition/4. Loading in the data.mp4 36.8 MB
  • 16. In-Depth Gradient Descent/1. Gradient Descent.mp4 36.6 MB
  • 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 36.5 MB
  • 16. In-Depth Gradient Descent/3. Momentum.mp4 35.9 MB
  • 15. In-Depth Loss Functions/1. Mean Squared Error.mp4 35.4 MB
  • 5. Convolutional Neural Networks/8. CNN Code Preparation (part 3).mp4 35.3 MB
  • 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 35.1 MB
  • 8. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.mp4 34.3 MB
  • 14. VIP Facial Recognition/7. Generating Generators.mp4 34.0 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).mp4 33.8 MB
  • 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 33.3 MB
  • 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 33.2 MB
  • 3. Machine Learning and Neurons/5. Moore's Law.mp4 32.1 MB
  • 14. VIP Facial Recognition/6. Converting the data into pairs.mp4 31.9 MB
  • 1. Introduction/3. Where to get the Code.mp4 30.9 MB
  • 14. VIP Facial Recognition/8. Creating the model and loss.mp4 30.8 MB
  • 3. Machine Learning and Neurons/12. Saving and Loading a Model.mp4 30.2 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 30.2 MB
  • 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 30.1 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.mp4 29.7 MB
  • 10. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.mp4 29.4 MB
  • 3. Machine Learning and Neurons/15. Model With Logits.mp4 28.6 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 28.2 MB
  • 3. Machine Learning and Neurons/9. Classification Code Preparation.mp4 27.8 MB
  • 14. VIP Facial Recognition/5. Splitting the data into train and test.mp4 27.6 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 26.2 MB
  • 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 25.7 MB
  • 14. VIP Facial Recognition/1. Facial Recognition Section Introduction.mp4 25.5 MB
  • 14. VIP Facial Recognition/3. Code Outline.mp4 25.0 MB
  • 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 24.8 MB
  • 5. Convolutional Neural Networks/12. Batch Normalization.mp4 24.6 MB
  • 11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 24.6 MB
  • 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 24.1 MB
  • 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 22.9 MB
  • 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 22.7 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).mp4 21.5 MB
  • 14. VIP Facial Recognition/10. Facial Recognition Section Summary.mp4 19.2 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 18.8 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 18.1 MB
  • 21. Appendix FAQ Finale/1. What is the Appendix.mp4 17.2 MB
  • 3. Machine Learning and Neurons/17. Suggestion Box.mp4 16.9 MB
  • 7. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.mp4 16.4 MB
  • 10. GANs (Generative Adversarial Networks)/4. Exercise DCGAN (Deep Convolutional GAN).mp4 16.1 MB
  • 3. Machine Learning and Neurons/11. Exercise Predicting Diabetes Onset.mp4 13.2 MB
  • 4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites.mp4 11.0 MB
  • 7. Natural Language Processing (NLP)/10. Exercise Sentiment Analysis.mp4 9.6 MB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Exercise More Forecasting.mp4 9.5 MB
  • 5. Convolutional Neural Networks/14. Exercise Facial Expression Recognition.mp4 8.7 MB
  • 12. Stock Trading Project with Deep Reinforcement Learning/10. Exercise Personalized Stock Trading Bot.mp4 8.2 MB
  • 9. Transfer Learning for Computer Vision/7. Exercise Transfer Learning.mp4 7.3 MB
  • 3. Machine Learning and Neurons/7. Exercise Real Estate Predictions.mp4 5.8 MB
  • 8. Recommender Systems/6. Exercise Book Recommendations.mp4 4.3 MB
  • 18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.vtt 28.6 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.vtt 26.4 kB
  • 5. Convolutional Neural Networks/5. CNN Architecture.vtt 24.9 kB
  • 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.vtt 23.4 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.vtt 22.9 kB
  • 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code Yourself (part 1).vtt 20.7 kB
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 20.7 kB
  • 5. Convolutional Neural Networks/6. CNN Code Preparation (part 1).vtt 20.6 kB
  • 4. Feedforward Artificial Neural Networks/4. Activation Functions.vtt 20.3 kB
  • 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.vtt 20.3 kB
  • 10. GANs (Generative Adversarial Networks)/1. GAN Theory.vtt 18.9 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).vtt 18.8 kB
  • 5. Convolutional Neural Networks/1. What is Convolution (part 1).vtt 18.8 kB
  • 5. Convolutional Neural Networks/4. Convolution on Color Images.vtt 18.7 kB
  • 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).vtt 18.3 kB
  • 3. Machine Learning and Neurons/2. Regression Basics.vtt 17.9 kB
  • 3. Machine Learning and Neurons/8. Linear Classification Basics.vtt 17.8 kB
  • 18. Setting up your Environment (FAQ by Student Request)/2. Windows-Focused Environment Setup 2018.vtt 17.8 kB
  • 3. Machine Learning and Neurons/1. What is Machine Learning.vtt 16.6 kB
  • 1. Introduction/2. Overview and Outline.vtt 16.1 kB
  • 7. Natural Language Processing (NLP)/3. Text Preprocessing (pt 1).vtt 16.0 kB
  • 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.vtt 16.0 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.vtt 15.9 kB
  • 3. Machine Learning and Neurons/4. Regression Notebook.vtt 15.7 kB
  • 8. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).vtt 15.6 kB
  • 16. In-Depth Gradient Descent/5. Adam (pt 1).vtt 15.0 kB
  • 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).vtt 14.7 kB
  • 3. Machine Learning and Neurons/3. Regression Code Preparation.vtt 14.6 kB
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).vtt 14.5 kB
  • 7. Natural Language Processing (NLP)/1. Embeddings.vtt 14.4 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).vtt 14.3 kB
  • 7. Natural Language Processing (NLP)/7. CNNs for Text.vtt 14.2 kB
  • 3. Machine Learning and Neurons/6. Moore's Law Notebook.vtt 14.2 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.vtt 14.1 kB
  • 7. Natural Language Processing (NLP)/4. Text Preprocessing (pt 2).vtt 13.8 kB
  • 4. Feedforward Artificial Neural Networks/6. How to Represent Images.vtt 13.8 kB
  • 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.vtt 13.6 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).vtt 13.4 kB
  • 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).vtt 13.3 kB
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).vtt 13.1 kB
  • 3. Machine Learning and Neurons/10. Classification Notebook.vtt 13.1 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.vtt 13.1 kB
  • 16. In-Depth Gradient Descent/6. Adam (pt 2).vtt 13.0 kB
  • 2. Google Colab/2. Uploading your own data to Google Colab.vtt 12.9 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).vtt 12.9 kB
  • 18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.9 kB
  • 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.vtt 12.8 kB
  • 3. Machine Learning and Neurons/16. Train Sets vs. Validation Sets vs. Test Sets.vtt 12.8 kB
  • 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.vtt 12.6 kB
  • 3. Machine Learning and Neurons/14. How does a model learn.vtt 12.4 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.vtt 12.4 kB
  • 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.vtt 12.2 kB
  • 5. Convolutional Neural Networks/9. CNN for Fashion MNIST.vtt 12.0 kB
  • 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).vtt 11.9 kB
  • 19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 2).vtt 11.7 kB
  • 4. Feedforward Artificial Neural Networks/9. ANN for Regression.vtt 11.6 kB
  • 13. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.vtt 11.5 kB
  • 14. VIP Facial Recognition/2. Siamese Networks.vtt 11.5 kB
  • 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).vtt 11.5 kB
  • 5. Convolutional Neural Networks/13. Improving CIFAR-10 Results.vtt 11.5 kB
  • 8. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.vtt 11.4 kB
  • 5. Convolutional Neural Networks/11. Data Augmentation.vtt 11.2 kB
  • 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.vtt 11.2 kB
  • 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).vtt 11.2 kB
  • 3. Machine Learning and Neurons/13. A Short Neuroscience Primer.vtt 11.0 kB
  • 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.vtt 11.0 kB
  • 4. Feedforward Artificial Neural Networks/2. Forward Propagation.vtt 10.9 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.vtt 10.8 kB
  • 2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 10.8 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.vtt 10.5 kB
  • 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).vtt 10.4 kB
  • 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.vtt 10.4 kB
  • 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.vtt 10.2 kB
  • 15. In-Depth Loss Functions/1. Mean Squared Error.vtt 10.1 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.vtt 9.9 kB
  • 8. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).vtt 9.8 kB
  • 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.vtt 9.7 kB
  • 10. GANs (Generative Adversarial Networks)/3. GAN Code.vtt 9.6 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.vtt 9.5 kB
  • 5. Convolutional Neural Networks/7. CNN Code Preparation (part 2).vtt 9.5 kB
  • 7. Natural Language Processing (NLP)/6. Text Classification with LSTMs.vtt 9.2 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.vtt 8.9 kB
  • 16. In-Depth Gradient Descent/1. Gradient Descent.vtt 8.8 kB
  • 15. In-Depth Loss Functions/3. Categorical Cross Entropy.vtt 8.6 kB
  • 14. VIP Facial Recognition/9. Accuracy and imbalanced classes.vtt 8.6 kB
  • 3. Machine Learning and Neurons/9. Classification Code Preparation.vtt 8.5 kB
  • 7. Natural Language Processing (NLP)/5. Text Preprocessing (pt 3).vtt 8.4 kB
  • 3. Machine Learning and Neurons/5. Moore's Law.vtt 8.2 kB
  • 5. Convolutional Neural Networks/10. CNN for CIFAR-10.vtt 8.2 kB
  • 7. Natural Language Processing (NLP)/9. VIP Making Predictions with a Trained NLP Model.vtt 8.2 kB
  • 9. Transfer Learning for Computer Vision/3. Large Datasets.vtt 8.2 kB
  • 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.vtt 8.0 kB
  • 13. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.vtt 7.9 kB
  • 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).vtt 7.9 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.vtt 7.8 kB
  • 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.vtt 7.7 kB
  • 10. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.vtt 7.6 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.vtt 7.6 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.vtt 7.5 kB
  • 17. Extras/1. Links To Colab Notebooks.html 7.4 kB
  • 5. Convolutional Neural Networks/3. What is Convolution (part 3).vtt 7.2 kB
  • 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.vtt 7.1 kB
  • 21. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.vtt 7.1 kB
  • 16. In-Depth Gradient Descent/3. Momentum.vtt 7.1 kB
  • 1. Introduction/3. Where to get the Code.vtt 6.9 kB
  • 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.vtt 6.9 kB
  • 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.vtt 6.7 kB
  • 15. In-Depth Loss Functions/2. Binary Cross Entropy.vtt 6.5 kB
  • 5. Convolutional Neural Networks/2. What is Convolution (part 2).vtt 6.5 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.vtt 6.5 kB
  • 5. Convolutional Neural Networks/8. CNN Code Preparation (part 3).vtt 6.4 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.vtt 6.2 kB
  • 14. VIP Facial Recognition/4. Loading in the data.vtt 6.2 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.vtt 6.1 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).vtt 6.1 kB
  • 3. Machine Learning and Neurons/12. Saving and Loading a Model.vtt 6.0 kB
  • 5. Convolutional Neural Networks/12. Batch Normalization.vtt 5.9 kB
  • 11. Deep Reinforcement Learning (Theory)/5. The Return.vtt 5.6 kB
  • 8. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.vtt 5.4 kB
  • 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.vtt 5.4 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).vtt 5.3 kB
  • 14. VIP Facial Recognition/6. Converting the data into pairs.vtt 5.3 kB
  • 14. VIP Facial Recognition/3. Code Outline.vtt 5.2 kB
  • 14. VIP Facial Recognition/7. Generating Generators.vtt 5.2 kB
  • 1. Introduction/1. Welcome.vtt 5.2 kB
  • 7. Natural Language Processing (NLP)/8. Text Classification with CNNs.vtt 5.1 kB
  • 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.vtt 4.9 kB
  • 3. Machine Learning and Neurons/15. Model With Logits.vtt 4.8 kB
  • 14. VIP Facial Recognition/8. Creating the model and loss.vtt 4.8 kB
  • 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).vtt 4.7 kB
  • 14. VIP Facial Recognition/5. Splitting the data into train and test.vtt 4.6 kB
  • 3. Machine Learning and Neurons/17. Suggestion Box.vtt 4.2 kB
  • 14. VIP Facial Recognition/1. Facial Recognition Section Introduction.vtt 4.1 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.vtt 4.1 kB
  • 7. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.vtt 4.1 kB
  • 14. VIP Facial Recognition/10. Facial Recognition Section Summary.vtt 4.0 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.vtt 4.0 kB
  • 21. Appendix FAQ Finale/1. What is the Appendix.vtt 3.4 kB
  • 10. GANs (Generative Adversarial Networks)/4. Exercise DCGAN (Deep Convolutional GAN).vtt 3.2 kB
  • 3. Machine Learning and Neurons/11. Exercise Predicting Diabetes Onset.vtt 2.9 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).vtt 2.9 kB
  • 4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites.vtt 2.6 kB
  • 7. Natural Language Processing (NLP)/10. Exercise Sentiment Analysis.vtt 2.3 kB
  • 6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Exercise More Forecasting.vtt 2.1 kB
  • 12. Stock Trading Project with Deep Reinforcement Learning/10. Exercise Personalized Stock Trading Bot.vtt 2.0 kB
  • 5. Convolutional Neural Networks/14. Exercise Facial Expression Recognition.vtt 1.7 kB
  • 9. Transfer Learning for Computer Vision/7. Exercise Transfer Learning.vtt 1.6 kB
  • 3. Machine Learning and Neurons/7. Exercise Real Estate Predictions.vtt 1.5 kB
  • 8. Recommender Systems/6. Exercise Book Recommendations.vtt 1.2 kB
  • 17. Extras/2. Links to VIP Notebooks.html 256 Bytes
  • 1. Introduction/[Tutorialsplanet.NET].url 128 Bytes
  • 10. GANs (Generative Adversarial Networks)/[Tutorialsplanet.NET].url 128 Bytes
  • 17. Extras/[Tutorialsplanet.NET].url 128 Bytes
  • 21. Appendix FAQ Finale/[Tutorialsplanet.NET].url 128 Bytes
  • 7. Natural Language Processing (NLP)/[Tutorialsplanet.NET].url 128 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes
  • 1. Introduction/3.1 Github Link.html 120 Bytes
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 0 Bytes
  • 20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 0 Bytes

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